list_models

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only says 'list available AI models' without describing side effects (likely none), auth needs, rate limits, or the structure of returned data. It adds little beyond the tool's name, leaving the agent to guess about defaults and response format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence, one line long, with no filler. However, it includes the inaccurate 'capability' term, which slightly detracts from its precision, but overall it is appropriately brief.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description should explain what the tool returns (e.g., model IDs, names, provider metadata). It doesn't. It also doesn't disclose default limit behavior or note the discrepancy around 'capability.' For a simple tool, it's under-specified regarding response structure and edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema has full descriptions for both parameters (100% coverage), the description introduces 'filtering by provider or capability,' but there is no 'capability' parameter in the schema. This is misleading and adds confusion. It omits the limit parameter and doesn't clarify the filtering semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'list' with the resource 'available AI models' and mentions optional filtering. This clearly differentiates it from sibling tools get_ai_recommendation and get_code_template, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context that this tool is for listing models and implies filtering options. It doesn't explicitly state when to avoid it or mention alternatives, but the sibling names make the use case obvious. There are no exclusions or prerequisites mentioned, which is acceptable for such a simple listing tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.